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already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n","Installing collected packages: xxhash, dill, multiprocess, datasets\n","Successfully installed datasets-2.13.1 dill-0.3.6 multiprocess-0.70.14 xxhash-3.2.0\n","Collecting accelerate\n","  Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in 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accelerate-0.21.0\n"]}],"source":["!pip install transformers==4.28.0\n","!pip install datasets\n","!pip install accelerate -U"]},{"cell_type":"code","source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QvRTCsEPvVCY","executionInfo":{"status":"ok","timestamp":1689563896831,"user_tz":420,"elapsed":5072,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"039e5575-8593-4551-f603-58e9bef29ab9"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1a1v06PvGaWmTCWvL33o2C133D90SRzMC\n","To: /content/cw_texts_clean_bert.csv\n","100% 59.5M/59.5M [00:01<00:00, 36.1MB/s]\n"]}]},{"cell_type":"code","source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-5 # decreased from 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 5 # reduced from 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    # for param in model.bert.parameters():\n","    #     param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=False, padding=False)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":690,"referenced_widgets":["f20aa41655f44f15b5c727e2325cc5c7","164b947188404df1bd6577b9b71e589f","7951ab4eefe943f89cc9fc032d3b8123","3ca8d9a329494eddb5bf36d555a0d5cd","76d1c3fcc25d49529c7d69462a6ad6ab","aa1eee5edd214b9bbec12acdce2fa23e","5249dbd0f9aa46f39ba43dfbfc5e59aa","4c102bdb85d24861a23baff9c16b4c15","ab847d84bda6424c9f3bc52872028dde","7a158c064e8b40f295ebf751229ff5de","5e51b6e8f7b24aa2adc2584db19ddf03","b823559ef61844cd85d0d8da6e32ac8e","114dce4868a04264b893cd0dbe7ceb68","4d242ea5c3414e9ca7c8197efca95b71","dec0cd75ee2d4ec6b05bae4b80b5e225","306e92652c684699a36cb3e7cbbdc390","f3de6d08940441628c3dcafdb9819d76","913f212e86e445bebda2769e67bdbeef","829b8d18c2e24072b8e941d3fd58079c","a98742f6539f4c25a1365a228afb1026","64a03000c01c4568a3b0a1dd081fc242","3e7b4bba3e24401f9e4a95730f7c49ae","e748ddcd4bdb493191efc9b161d9e8c9","4491465d75ca47ef9086b3146b5c5890","3433838daa244fab93e41869ca23b195","df0ecd5663a9456c9be3209719778934","ef92229c29f445be916186e3cf608dab","f23fac05634d4ab4a589004d3dfb5647","0db4a097582342eba09e07ad6e0067f5","18015f2175d044f9a21ed82a746eb530","bddb1efeb9e542919fcc7700fd53fa66","ea5e7503aa4a4177bebc072de3b588a4","a3265d96ed784568a95410016514c472","139c92318e82474d871082c2d49a19ed","410b262cd3ef4cd1ba848d1abd0c2697","86bab8b53536455b9df7e68e6bc275b3","be6cc660abe3436b97eed6bf2f3af8c0","0d91fdfd6efa482dbbd479a0e5e0f5e4","27ea9ac34afe4774ab3401ec7b34507b","f6bbccb1f20f4960b461b1b27d271736","8bd456db5da34243877c002068764a4f","30498131ef3d4aaebeabe937c945fa35","bbc4717214db4f7aa94f23852fbad741","1cdd28417a644e48a62011354054fa53","6e73bdf3aef9423ab49fb16537c63f6a","824f256e9898435dbae27b7f26bfd1f9","8124f6ca42634cb5940335c2cb664876","e056ecd0c2564bf1b213c71d1be89175","d9a274a215fd48fcb330705db97af611","eb08d188d1694cbe9485f1ca15ca919a","d9411a9ed2dd4667ab16b8353d1649c1","24f54a93b94640febc8e64b52779d69a","36142fcd33d74ac4a89b3de2cf553a8b","be46a970a07d458b81c7160b09f43f8c","c81a47ab711a454c9d9a0cda70beb3fc","fa7be6db98dd4e899060f70f8bed6dea","0b881f4a620a4dca92098c8d42a31a83","42f96e72bfe8486db3d32be6a3447c08","eb2c0be5171d4da3b4fc8b39eb37a6c3","a77d38242dc34db5bdab753e0544e5f7","10d4516324024b68aaf71231ccd7471b","e43101337b0941e1bacf56d508492c3c","58dac0bc83e4454b8cdce2926e7d2e24","588d6c3bc450444987ff7c719c46fc4f","d28eb0988ea1413d94dd50d14829b6cb","f3ec93db34c947d08d374471bec47d49","05271b76546a4ef99f45ee4f2bbfbd36","6d0de7e18a704286b04a3fc7ce434424","b774bc2290434e01979df94bef062b4b","82e3a6c54e554b22afa9560ed65fb7a1","16d2614e14c744c4a5883dde3dbb5a8d","2213755639344366abbdce92d47562fe","29a259c56a834f6fa3cf0cd693abda24","16c4b7c9f9c64cbb8d19c89032597f93","02ba502937e14c68a63b1b64883a15d7","33d446853e9442bcb45f37acc203a679","fc81a35096fe4629a4a0b2f7abe55d4d","6750c212ebba4ee09ac720378b0b0c9f","b87ac00852834db7822390da5167fd3b","c29658a9d8704802a61282a4aa05cb03","1e40aee3e2c04fbea1191e0ee5b868de","f09893473f3c4ff4b3da7d344319934b","127aa802d7d2484d9e4667b999d03628","99b66f995c6b4cee89cad95aa82d7bc3","1089c4131c55449a848c8071938bc168","54442e08f1884f469dbc05d683464367","0885504f204d4c959cecd4f752090b86","f1078937744d4070a926f3bc5a2ea2dd"]},"id":"_UUtlbRAySQ_","outputId":"eae32062-d660-40cc-9bae-ab1ff744d3db","executionInfo":{"status":"ok","timestamp":1689563940846,"user_tz":420,"elapsed":44021,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}}},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-3-f941a3051873>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-f941a3051873>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-f941a3051873>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-f941a3051873>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","  1%|          | 191/22572 [00:00<00:11, 1907.48it/s]<ipython-input-3-f941a3051873>:32: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:08<00:00, 2580.86it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f20aa41655f44f15b5c727e2325cc5c7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b823559ef61844cd85d0d8da6e32ac8e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e748ddcd4bdb493191efc9b161d9e8c9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"139c92318e82474d871082c2d49a19ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6e73bdf3aef9423ab49fb16537c63f6a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/21924 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa7be6db98dd4e899060f70f8bed6dea"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/648 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"05271b76546a4ef99f45ee4f2bbfbd36"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1296 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6750c212ebba4ee09ac720378b0b0c9f"}},"metadata":{}}]},{"cell_type":"code","source":["import numpy as np\n","sequence_lengths = []\n","for split in ds:\n","    for input_ids in ds[split][\"input_ids\"]:\n","        sequence_lengths.append(len(input_ids))\n","print(\"#\" * 20)\n","print(\"Summary statisticis\")\n","print(\"Number of samples:\", len(sequence_lengths))\n","print(\"Mean:\", round(np.mean(sequence_lengths)))\n","print(\"Max\", np.max(sequence_lengths))\n","print(\"Min\", np.min(sequence_lengths))\n","print(\"Standard deviation\", round(np.std(sequence_lengths)))\n","print(\"Number of samples with sequence length > 256:\", sum([l > 256 for l in sequence_lengths]))\n","print(\"Number of samples with sequence length > 512:\", sum([l > 512 for l in sequence_lengths]))\n","print(\"#\" * 20)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0eUfRuIZ3B4O","executionInfo":{"status":"ok","timestamp":1689564992477,"user_tz":420,"elapsed":1653,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"26e9d579-c2f2-4607-ac97-a212192cc396"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["####################\n","Summary statisticis\n","Number of samples: 23868\n","Mean: 112\n","Max 873\n","Min 4\n","Standard deviation 134\n","Number of samples with sequence length > 256: 3386\n","Number of samples with sequence length > 512: 261\n","####################\n"]}]}]}